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Financial Econometrics

Recent years have witnessed a growing need for econometric methods in financial research and practice. As a result, financial econometrics has become one of the most active areas of research in econometrics. This is documented by the award of the Nobel Prize 2003 to Robert F. Engle for his contribution to the modelling of time-varying asset return volatility and the award of the 2013 Nobel Prize to Lars Peter Hansen for his pioneering work on the empirical analysis of asset prices.


This course aims to provide students an introduction into the field and an overview of the most important topics and techniques. Having predominantly an applied focus, it attempts to balance between derivations of basic theoretical relations, fundamental methodology, the analysis of specific financial econometric models, applications thereof as well as the discussion of important empirical findings.


The course deals with fundamental time series techniques to model and to predict financial data, the modelling of time-varying volatility as well as the estimation and testing of asset pricing models. Current topics in modern financial econometric research, such as the modelling of realized volatility as well as the analysis of financial high-frequency data is covered as well.
Moreover, an important objective is to provide a comprehensive knowledge to do empirical work in financial research and practice. Therefore, a part of the course consists of practical exercises where students are instructed to apply econometric concepts to real financial data. In this context, students will be introduced to basic programming and application steps using the statistical software package R.

 

Course Outline


1. Financial Returns: Basic Concepts and Properties
  1.1. Introduction
  1.2. Financial Time Series
    1.2.1. Illustrations
    1.2.2. Returns on Financial Assets
  1.3. Distributional Properties of Financial Returns
    1.3.1. Conditional and Marginal Distributions
    1.3.2. Evaluating Marginal Distributions
    1.3.3. Empirical Evidence
    1.3.4. Distributions for Returns
 
2. Econometric Foundations
  2.1. Principles of Linear Regression
    2.1.1. Basic Principles of Inference
    2.1.2. Ordinary Least Squares Estimation
    2.1.3. Hypothesis Testing
    2.1.4. Generalized Error Term Structures
  2.2. Basic Concepts of Asymptotic Theory
    2.2.1. Convergence of Random Sequences
    2.2.2. Laws of Large Numbers and Central Limit Theorems
    2.2.3. Large-Sample Properties of the OLS Estimator
  2.3. Maximum Likelihood Estimation
    2.3.1. Basic Concepts
    2.3.2. Asymptotic Properties

3. Time Series Foundations and Price Dynamics
  3.1. Foundations in Time Series Analysis
    3.1.1. Basic Concepts
    3.1.2. Stationarity and Ergodicity
    3.1.3. Testing for White Noise
    3.1.4. Linear Processes
    3.1.5. ARMA Processes
    3.1.6. Estimating ARMA Processes
    3.1.7. Integrated Time Series
    3.1.8. Box-Jenkins Analysis
    3.1.9. Fractional ARIMA Processes
  3.2. Financial Prices and Returns
    3.2.1. The Efficient Market Hypothesis
    3.2.2. Random Walk Tests
    3.2.3. Empirical Evidence

4. Modelling Conditional Autoregressive Heteroscedasticity
  4.1. Introduction
    4.1.1. Different Volatility Concepts
    4.1.2. Some Empirical Facts
    4.1.3. Why Should We Care?
  4.2. GARCH Models
    4.2.1. Model Structure
    4.2.2. The ARCH Model
    4.2.3. The GARCH Model
    4.2.4. Further GARCH Specifications
    4.2.5. Estimation and Testing
    4.2.6. Multivariate GARCH Models
    4.2.7. Value-at-Risk Estimation Using GARCH

5. Realized Volatility and Stochastic Volatility
  5.1. Realized Volatility
    5.1.1. Basic Concepts of Continuous-Time Processes
    5.1.2. The Realized Volatility Estimator
    5.1.3. Empirical Properties of the RV Estimator
    5.1.4. RV and Market Microstructure Noise
    5.1.5. Realized Correlation
  5.2. Stochastic Volatility
    5.2.1. Motivation
    5.2.2. The Standard SV Model
    5.2.3. Empirical Evidence

6. Estimating and Testing Asset Pricing Models
  6.1. Stochastic Discount Factor Based Asset Pricing
    6.1.1. The Stochastic Discount Factor
    6.1.2. The Consumption Based Model
  6.2. Generalized Method of Moments Estimation
    6.2.1. Principle of Moments Estimation
    6.2.2. Generalized Method of Moments Estimation
    6.2.3. GMM Estimation of Asset Pricing Models
  6.3. The Capital Asset Pricing Model (CAPM)
    6.3.1. Beta Representations
    6.3.2. Two Period Quadratic Utility
    6.3.3. Exponential Utility and Normality
    6.3.4. Testable Implications
  6.4. Factor Pricing Regressions
    6.4.1. Seemingly Unrelated Regression
    6.4.2. Time Series Regressions
    6.4.3. Fama-MacBeth Regressions
    6.4.4. The Fama-French Three-Factor Model

7. Modelling High-Frequency Financial Data
  7.1. Financial Transaction Data
    7.1.1. Market Microstructure Relationships
    7.1.2. Properties of Financial Transaction Data
  7.2. Modelling Transaction Price Processes
    7.2.1. Decomposition of the Price Process
    7.2.2. Modelling Prices and Quotes
  7.3. Multiplicative Error Models
    7.3.1. Autoregressive Conditional Duration (ACD) Models
    7.3.2. Theoretical Properties of ACD Models
    7.3.3. Generalized ACD Models
    7.3.4. Multivariate Models for Trading Processes

Literature:

  • Andersen T. G. and Benzoni L. (2009), in: ''Handbook of Financial Time Series'', ed. Andersen, T.G., Davis, R. A., Kreiß, J.-P. and Mikosch, T., Springer, p. 556-576
  • Chris Brooks, (2008), Introductory Econometrics for Finance, Cambridge University Press, (selected chapters)
  • Campbell, J. Y., A. W. Lo, and A. C. MacKinlay (1997): ''The Econometrics of Financial Markets'', Princeton University Press
  • Cochrane, J. H. (2005): “Asset Pricing”, revised edition, Princeton University Press
  • Davidson, R., and J. G. MacKinnon (2004): “Econometric Theory and Methods”, Oxford University Press
  • Franses, P. H., and D. van Dijk (2000): ''Non-Linear Time Series Models in Empirical Finance'', Cambridge University Press
  • Härdle, W., Hautsch, N., and Overbeck L. (2008): ''Applied Quantitative Finance'', 2nd ed., Springer.
  • Hamilton, J. D. (1994): ''Time Series Analysis'', Princeton University Press
  • Hautsch, N. (2012): “Econometrics of Financial High-Frequency Data”, Springer.
  • Stock, J. H. and M. W. Watson (2012): Introduction to Econometrics, 3rd edition, Pearson
  • Taylor, S. J. (2005): ''Asset Price Dynamics, Volatility, and Prediction'', Princeton University Press
  • Tsay, R. S. (2010): ''Analysis of Financial Time Series: Financial Econometrics'', Wiley, 3rd edition
Institut für Finanzwirtschaft
Fakultät für Wirtschaftswissenschaften
Universität Wien

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